Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans.
This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets.
A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results.
An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.
Despite strong evidence supporting guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF), a significant gap persists in the consistent application of these therapies. This shortfall has prompted organizations like the American College of Cardiology to recommend leveraging electronic health records (EHR) to optimize GDMT. This paper discusses the development of SmartHF, a clinical decision support system designed to enhance therapy adherence by effectively linking Fast Healthcare Interoperability Resources (FHIR)-based medication data with clinical algorithms tailored for the management of HFrEF.
The SmartHF system integrates FHIR-based medication data with clinical algorithms through a multi-step approach. Central to this process is data from CodeRx, a platform that utilizes streamlined data pipelines to map medication products to their ingredients using RxNorm. The methodology addresses the challenge of interpreting both structured and unstructured medication instructions, ensuring a precise linkage of product identifiers to algorithm-relevant ingredients and their corresponding strengths. Specific attention is given to the data granularity needed for distinguishing precise ingredients within complex formulations, such as sacubitril/valsartan and metoprolol salt form variants.
The deployment of SmartHF involved rigorous testing using actual and synthetic patient datasets to validate its functionality. Results demonstrated the system's ability to process FHIR MedicationRequest data resources accurately, convert free-text dosing instructions into usable formats, and handle edge cases, including non-standard products and missing dose information.
This article describes the potential of FHIR-based medication data integration for enhancing clinical decision support tools and improving care quality. It highlights the challenges and solutions for this integration.
This article shares insights from the Learning Health Systems Symposium, “Charting the Future of Saskatchewan Healthcare: Generating Value within our Health Systems.”
Patient Partners and over 100 professionals in the fields of research, healthcare, and policy joined the Saskatchewan Centre for Patient-Oriented Research (SCPOR) in Regina, Saskatchewan Canada, for a day filled with knowledge about learning health systems (LHSs). Attendees provided key insights on how to build provincial capacity in LHSs and thereby improve the health of people in Saskatchewan.
Key insights from the symposium included strengthening meaningful partnerships with patients and the community; establishing a shared vision for LHSs; harmonizing conflicting priorities; removing silos; recognizing the natural tensions between academia and the healthcare system; and building on and aligning infrastructure to support LHSs development.
The LHSs symposium provided a way for health system leaders, Patient Partners, researchers, clinicians, and students to develop a common understanding of LHSs and envision a future for LHSs in Saskatchewan, Canada. Key next steps were to strengthen patient and community engagement, work collaboratively to establish a shared vision of LHSs, better understand existing assets to support LHSs, and identify opportunities to build future LHSs infrastructure.
To support a learning health system, we aimed to develop processes and tools to enable collaboration between the Veterans Health Administration (VHA) operations and research sectors in the implementation of promising healthcare innovations.
The collaboration process involved: (1) holding foundational partnership meetings between operations and health research sector leadership (2) reviewing existing research–operations collaborations frameworks, (3) developing a framework with criteria for systematically categorizing innovations into defined pathways that integrate research and operational considerations, and (4) adapting known participatory approaches to collect information for productive collaboration. Clearly delineating the skills, goals, and perspectives of the innovation and research sectors during partnership meetings enabled assessment of the value and trade-offs of advancing an innovation into practice. The literature review of existing frameworks yielded heterogeneous objectives, domains, and criteria for evaluation. Across framework domains, commonalities (e.g., alignment with institutional goals), differences (e.g., attention to sustainment), and gaps (e.g., health equity focus) were noted. We developed five research–innovation partnership pathways and criteria to vet and categorize potential innovations (i.e., evidence for effectiveness and implementation, risks, equity, feasibility, and sustainability). We developed a menu of participatory processes to elicit feedback on innovations.
We applied this process with an innovation to refine and evaluate the process, pathways, and criteria. Surveys of diverse partners (clinicians, administrators, researchers) ranked the innovation using our developed criteria, and placed the innovation on a pathway, which then helped guide next steps for evaluating the innovations. Overall, the process was feasible. We were able to categorize and plan next steps for promising innovations.
This theoretically grounded, iterative process may serve as a blueprint to accelerate the implementation of healthcare innovations. Through these intentional and participatory processes to engage operations innovators and health services researchers we can speed the delivery of promising innovations that impact patient care.
A learning health system requires infrastructure to support its learning community. The World Health Organization's OpenWHO.org, launched in 2017, aimed to provide such infrastructure through a learning platform to equip emergency responders, health workers, and other stakeholders with critical knowledge and skills to manage health crises effectively. The free, open-access, self-paced learning platform addressed a wide range of public health topics. This experience report summarizes OpenWHO's impact over its 7 years as a cornerstone platform for just-in-time online learning in health emergencies.
A descriptive review of practice-led literature at WHO was used to report on the lessons learned to prepare for the next pandemic. The research focused on the design, development, and delivery of online courses via the platform. Our analysis comprises insights gathered from 32 studies.
Over 7 years, OpenWHO reached 9.2 million enrolments globally, delivering over 315 public health courses across 75 languages with use cases in all countries and territories. The platform has been instrumental in enhancing global health capacity, disseminating knowledge, and improving preparedness for health crises, including major outbreaks such as COVID-19. Key lessons learned include providing learning content in just-in-time modalities, addressing digital divides, and adapting content for diverse cultural and linguistic contexts with an endeavor to ensure equity, inclusivity, and scalability in global health learning.
OpenWHO has significantly contributed to empowering the global health workforce, driving low-bandwidth innovation in online education, and ensuring equitable access to critical knowledge during emergencies. Beyond documenting OpenWHO's outcomes, this experience report provides a transferable framework that can guide future Massive Open Online Courses initiatives in delivering equitable, just-in-time learning during public health emergencies.
Access improvement is a fundamental component of value-based healthcare as it inherently promotes quality by eliminating chokepoints, redundancies, and inefficiencies that could hinder the provisioning of timely care. Yet as healthcare organizations struggle with cost containment, the question of how to most effectively enhance access remains largely unsolved. Given the economic, regulatory, and social forces across the healthcare marketplace, the critical importance of access in optimizing efficiency is increasingly being recognized. The purpose of this review is to thus present a practical framework that offers healthcare organizations an actionable, thematic-based foundation for approaching access improvement.
An interpretive synthesis of health access as it relates to the timely, satisfactory, and sustainable receipt of services was presented while addressing operational constraints, inclusivity and underlying determinants of health. The criticality of this concept, spanning the entire healthcare continuum and encompassing all aspects of care delivery, from making an initial appointment to completing treatment and being followed thereafter, was evaluated. Empirical lessons were highlighted for discussion.
Given the sense of urgency that exists around this issue, the potential pitfalls of “hurrying” to initiate access improvement are frequently overlooked by health systems. Considerations related to workforce shortages, resource limitations, logistical coordination, workflow processes, space capacity, provider availability, and organizational culture and hierarchy, among others, need to be methodically addressed so as not to introduce new inefficiencies into a healthcare environment that is already viewed by many as overly bureaucratic. Based on the core themes that emerged, a conceptual framework for access improvement centered on strategy, alignment, execution, adaptation, and reflection was developed.
The design, planning, and operationalization of access improvement initiatives in healthcare require meticulous organizational preparation and a deliberate leadership approach incorporating principles of change management. Success will be dependent on achieving the appropriate balance between speed, purpose, and precision.
Maternal harm disproportionately affects Black birthing individuals, with systemic and provider-related factors contributing significantly to preventable severe maternal morbidity (SMM) and maternal mortality. Despite the severity of the crisis, traditional maternal safety approaches, which typically exclude patient voices, narrowly focus on severe harm events rather than conducting full-spectrum safety surveillance, which limits proactive intervention strategies.
This study applied the National Academies of Medicine's Learning Health System (LHS) principles to develop a comprehensive maternal safety framework. We employed a mixed-methods approach integrating patient-reported experiences of cases (patients experiencing severe maternal morbidity and/or postpartum readmission) and controls, provider perspectives, clinical informatics, natural language processing of clinical notes, and chart reviews to identify factors contributing to and mitigating maternal harm. We convened an interdisciplinary expert panel to synthesize findings into actionable recommendations for a Perinatal Safety Framework and a Perinatal Strategy Guide.
The Perinatal Maternal Safety Framework was developed to define the maternal safety continuum. Six key features emerged, including: (1) three distinct starting points (pregnancy, birth, postpartum), (2) variability in baseline circumstances, (3) a broad definition of maternal harm encompassing physical, emotional, and psychological factors, (4) adaptation of established safety models, (5) six categories of contributing and mitigating factors, and (6) status factors influencing maternal safety outcomes. The Perinatal Strategy Guide outlines evidence-based strategies addressing structural and systemic factors affecting maternal safety.
A comprehensive maternal safety framework integrating full-spectrum safety surveillance and patient-centered reporting is critical to addressing maternal health disparities. By applying LHS principles, this study provides a data-driven, equity-focused approach to improving maternal safety. Implementation of the Perinatal Strategy Guide will require interdisciplinary collaboration and engagement of individuals with lived experience to drive systemic change.
The Swiss Learning Health System (SLHS), funded from 2017 to 2025, facilitated the movement of research to practice and policy, responding to national calls to enhance health services research and build research scientist capacity. This study evaluates the SLHS to understand how Learning Health System (LHS) science can provide a foundation for Knowledge Translation (KT) platforms at a national level, identifying successes, challenges, and lessons learned from capacity building and institutionalizing KT in support of evidence-informed decision-making (EIDM) in health policy and practice.
We employed a mixed-methods approach from September to December 2023, using the SLHS program aims and the Knowledge-to-Action framework to inform the study's conceptualization. Data collection involved two workshops with over 40 SLHS members, a survey of 39 members, and in-depth interviews with 10 key informants, analyzed using descriptive and thematic methods.
Capacity-building efforts prompting a cultural shift by training research scientists to adopt a science-policy-practice mindset were the most common successes captured. The LHS and KT approaches aided in dismantling silos and encouraged community building through participatory methods. An important lesson learned is the value of co-creation involving key partners, especially patients, in the research process to strengthen relevance, issue prioritization, and evidence use. However, challenges persisted in adequately tailoring and transferring knowledge, highlighting the need for more consistent engagement with community partners to enhance the impact and relevance of KT efforts.
This study demonstrated that the SLHS is a valued initiative for capacity building, while highlighting the need to strengthen co-creation and refine strategies for adapting research evidence for EIDM in health policy and practice.
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